Genome-Wide Patterns of Genetic Variation within and among Alternative Selective Regimes
Evolutionary biologists seek to understand the factors affecting genetic variation. While it is intuitive that environmental heterogeneity should increase levels of variation, theoretical models showed that spatial and temporal heterogeneity differ in how likely they are to maintain polymorphisms affecting fitness. We evolved experimental populations of fruit flies in constant environments or in temporally or spatially varying environments, then examined levels of sequence variation across the genome. For sites associated with ecological selection, polymorphism patterns matched the theoretical expectations with variation greatest in populations evolving in spatially heterogeneous environments, less variation in populations evolving in temporally heterogeneous environments, and least variation in populations evolving in constant environments. However, a different pattern was observed at sites not associated with differential ecological selection (i.e., most of the genome). For these sites, levels of variation were highest at spatially heterogeneous populations but lowest for temporally heterogeneous populations. Populations evolving under temporal heterogeneity also showed the greatest differentiation from one another, suggesting that this selection regime caused more genetic drift than other selection regimes. These results illustrate that environmental heterogeneity affects levels of variation not only at sites subject to differential ecological selection but also genome-wide, though spatial and temporal heterogeneity affect diversity differently.
Vyšlo v časopise:
Genome-Wide Patterns of Genetic Variation within and among Alternative Selective Regimes. PLoS Genet 10(8): e32767. doi:10.1371/journal.pgen.1004527
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pgen.1004527
Souhrn
Evolutionary biologists seek to understand the factors affecting genetic variation. While it is intuitive that environmental heterogeneity should increase levels of variation, theoretical models showed that spatial and temporal heterogeneity differ in how likely they are to maintain polymorphisms affecting fitness. We evolved experimental populations of fruit flies in constant environments or in temporally or spatially varying environments, then examined levels of sequence variation across the genome. For sites associated with ecological selection, polymorphism patterns matched the theoretical expectations with variation greatest in populations evolving in spatially heterogeneous environments, less variation in populations evolving in temporally heterogeneous environments, and least variation in populations evolving in constant environments. However, a different pattern was observed at sites not associated with differential ecological selection (i.e., most of the genome). For these sites, levels of variation were highest at spatially heterogeneous populations but lowest for temporally heterogeneous populations. Populations evolving under temporal heterogeneity also showed the greatest differentiation from one another, suggesting that this selection regime caused more genetic drift than other selection regimes. These results illustrate that environmental heterogeneity affects levels of variation not only at sites subject to differential ecological selection but also genome-wide, though spatial and temporal heterogeneity affect diversity differently.
Zdroje
1. Mitchell-OldsT, WillisJH, GoldsteinDB (2007) Which evolutionary processes influence natural genetic variation for phenotypic traits? Nat Rev Genet 8: 845–856.
2. LefflerEM, BullaugheyK, MatuteDR, MeyerWK, SégurelL, et al. (2012) Revisiting an old riddle: what determines genetic diversity levels within species? PLoS Biol 10: e1001388 doi:10.1371/journal.pbio.1001388
3. Charlesworth B, Hughes KA (2000) The maintenance of genetic variation in life history traits. Pp. 369–391 in R. S. Singh and C. B. Krimbas, eds. Evolutionary Genetics from Molecules to Morphology. Cambridge University Press, Cambridge, UK.
4. JohnsonT, BartonN (2005) Theoretical models of selection and mutation on quantitative traits. Philosophical Transactions of the Royal Society B: Biological Sciences 360: 1411–1425.
5. LeveneH (1953) Genetic equilibrium when more than one niche is available. Am Nat 87: 331–333.
6. FelsensteinJ (1976) The theoretical population genetics of variable selection and migration. Annu Rev Genet 10: 253–280.
7. DempsterER (1955) Maintenance of genetic heterogeneity. Cold Spring Harbor Symp. Quant Biol 20: 25–32.
8. HillWG, RobertsonA (1966) The effect of linkage on limits to artificial selection. Genetical Research 8: 269–294.
9. CharlesworthD, CharlesworthB, MorganMT (1995) The pattern of neutral molecular variation under the background selection model. Genetics 141: 1619–1632.
10. CharlesworthD (2006) Balancing selection and its effects on sequences in nearby genome regions. PLoS Genet 2: e64 doi:10.1371/journal.pgen.0020064
11. BeardmoreJA (1961) Diurnal temperature fluctuation and genetic variance in Drosophila populations. Nature 189: 162–163.
12. LongT (1970) Genetic effects of fluctuating temperature in populations of Drosophila melanogaster. Genetics 66: 401–416.
13. MackayTFC (1981) Genetic variation in varying environments. Genetical Research 37: 79–93.
14. RiddleRA, DawsonPS, ZirkleDF (1986) An experimental test of the relationship between genetic variation and environmental variation in tribolium flour beetles. Genetics 113: 391–404.
15. YeamanS, ChenY, WhitlockMC (2010) No effect of environmental heterogeneity on the maintenance of genetic variation in wing shape in drosophila melanogaster. Evolution 64: 3398–3408.
16. VenailPA, KaltzO, Olivierii, PommierT, MouquetN (2011) Diversification in temporally heterogeneous environments: effect of the grain in experimental bacterial populations. Journal of Evolutionary Biology 24: 2485–2495.
17. HallssonLR, BjörklundM (2012) Selection in a fluctuating environment leads to decreased genetic variation and facilitates the evolution of phenotypic plasticity. Journal of Evolutionary Biology 25: 1275–1290.
18. PowellJR (1971) Genetic polymorphisms in varied environments. Science 174: 1035–1036.
19. McDonaldJF, AyalaFJ (1974) Genetic response to environmental heterogeneity. Nature 250: 572–574.
20. HaleyCS, BirleyAJ (1983) The genetical response to natural selection by varied environments. II. Observations on replicate populations in spatially varied laboratory environments. Heredity 51: 581–606.
21. BurkeMK, DunhamJP, ShahrestaniP, ThorntonKR, RoseMR, et al. (2010) Genome-wide analysis of a long-term evolution experiment with Drosophila. Nature 467: 587–590.
22. TurnerTL, StewartAD, FieldsAT, RiceWR, TaroneAM (2011) Population-Based Resequencing of Experimentally Evolved Populations Reveals the Genetic Basis of Body Size Variation in Drosophila melanogaster. PLoS Genet 7: e1001336 doi:10.1371/journal.pgen.1001336
23. Orozco-terWengelP, KapunM, NolteV, KoflerR, FlattT, et al. (2012) Adaptation of Drosophila to a novel laboratory environment reveals temporally heterogeneous trajectories of selected alleles. Molecular Ecology 21: 4931–4941.
24. RemolinaSC, ChangPL, LeipsJ, NuzhdinSV, HughesKA (2012) Genomic basis of aging and life-history evolution in Drosophila melanogaster. Evolution 66: 3390–3403.
25. LongTAF, RoweL, AgrawalAF (2013) The Effects of Selective History and Environmental Heterogeneity on Inbreeding Depression in Experimental Populations of Drosophila melanogaster. The American Naturalist 181: 532–544.
26. McDonald JH (2009) Handbook of Biological Statistics (2nd ed.). Sparky House Publishing, Baltimore, Maryland 88–94.
27. EgliD, DomènechJ, SelvarajA, BalamuruganK, HuaH, et al. (2006) The four members of the Drosophila metallothionein family exhibit distinct yet overlapping roles in heavy metal homeostasis and detoxification. Genes to Cells 11: 647–658.
28. YepiskoposyanH, EgliD, FergestadT, SelvarajA, TreiberC, et al. (2006) Transcriptome response to heavy metal stress in Drosophila reveals a new zinc transporter that confers resistance to zinc. Nucleic Acids Research 34: 4866–4877.
29. StergiopoulosK, CabreroP, DaviesSA, DowJAT (2009) Salty dog, an SLC5 symporter, modulates Drosophila response to salt stress. Physiological Genomics 37: 1–11.
30. ChoiS, KimW, ChungJ (2011) Drosophila Salt-inducible Kinase and Element-binding Protein. J Biol Chem 286: 2658–2664.
31. KapunM, SchalkwykHV, McallisterB, FlattT, SchlottererC (2014) Inference of chromosomal inversion dynamics from Pool-Seq data in natural and laboratory populations of Drosophila melanogaster. Molecular Ecology 23: 1813–1827.
32. Baldwin-BrownJG, LongAD, ThorntonKR (2014) The Power to Detect Quantitative Trait Loci Using Resequenced, Experimentally Evolved Populations of Diploid, Sexual Organisms. Mol Biol Evol 31: 1040–1055.
33. ToblerR, et al. (2014) Massive habitat-specific genomic response in D. melanogaster populations during experimental evolution in hot and cold environments. Mol Biol Evol 31: 364–375.
34. KoflerR, SchlöttererC (2013) A guide for the design of evolve and resequencing studies. Mol Biol Evol 31: 474–83 doi: 10.1093/molbev/mst221
35. KoflerR, Orozco-terWENGELP, De MaioN, PandeyRV, NolteV, et al. (2011) PoPoolation: A toolbox for population genetic analysis of next generation sequencing data from pooled individuals. PLoS ONE 6: e15925 doi:10.1371/journal.pone.0015925
36. FutschikA, SchlöttererC (2010) The next generation of molecular markers from massively parallel sequencing of pooled DNA samples. Genetics 186: 207–218.
37. JakobssonM, EdgeMD, RosenbergNA (2013) The relationship between FST and the frequency of the most frequent allele. Genetics 193: 515–528.
38. NordborgM (1997) Structured coalescent processes on different time scales. Genetics 146: 1501–1514.
39. BartonNH (2000) Genetic hitchhiking. Philos Trans R Soc Lond B 355: 1553–1562.
40. GillespieJH (1997) Junk ain't what junk does: neutral alleles in a selected context. Gene 205: 291–299.
41. TaylorJE (2013) The effect of fluctuating selection on the genealogy at a linked site. Theor Pop Bio 87: 34–50.
42. KassenR (2002) The experimental evolution of specialists, generalists, and the maintenance of diversity. Journal of Evolutionary Biology 15: 173–190.
43. TurelliM, BartonNH (2004) Polygenic variation maintained by balancing selection: pleiotropy, sex-dependent allelic effects and G×Polygenic varia. Genetics 166: 1053–1079.
44. QianW, DiMa, XiaoC, WangZ, ZhangJ (2012) The genomic landscape and evolutionary resolution of antagonistic pleiotropy in Yeast. Cell Reports 2: 1399–1410.
45. AndersonJT, WillisJH, Mitchell-OldsT (2011) Evolutionary genetics of plant adaptation. Trends in Genetics 27: 258–266.
46. LeinonenPH, RemingtonDL, LeppäläJ, SavolainenO (2012) Genetic basis of local adaptation and flowering time variation in Arabidopsis lyrata. Molecular Ecology 22: 709–723.
47. AndersonJT, LeeC-R, RushworthCA, ColauttiRI, Mitchell-OldsT (2012) Genetic trade-offs and conditional neutrality contribute to local adaptation. Molecular Ecology 22: 699–708.
48. SchmidtPS, CondeDR (2006) Environmental heterogeneity and the maintenance of genetic variation for reproductive diapause in Drosophila melanogaster. Evolution 60: 1602–1611.
49. KaweckiTJ, LenskiRE, EbertD, HollisB, OlivieriI, et al. (2012) Experimental evolution. Trends in Ecology & Evolution 27: 547–560.
50. WallaceB (1975) Hard and soft selection revisited. Evolution 29: 465–473.
51. LiH, DurbinR (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25: 1754–1760.
52. LiH, HandsakerB, WysokerA, FennellT, RuanJ, et al. (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25: 2078–2079.
53. KoflerR, PandeyRV, SchlöttererC (2011) PoPoolation2: identifying differentiation between populations using sequencing of pooled DNA samples (Pool-Seq). Bioinformatics 27: 3435–3436.
54. BenjaminiY, YekutieliD (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29: 1165–1188.
55. Wickham H (2009) ggplot2: elegant graphics for data analysis. New York: Springer
56. CoopG, PickrellJK, NovembreJ, KudaravalliS, LiJ, et al. (2009) The role of geography in human adaptation. PLoS Genet 5: e1000500 doi:10.1371/journal.pgen.1000500
57. FumagalliM, SironiM, PozzoliU, Ferrer-AdmettlaA, PattiniL, et al. (2011) Signatures of environmental genetic adaptation pinpoint pathogens as the main selective pressure through human evolution. PLoS Genet 7: e1002355 doi:10.1371/journal.pgen.1002355.t003
58. McQuiltonP, St PierreSE, ThurmondJ (2011) the FlyBase Consortium (2011) FlyBase 101 - the basics of navigating FlyBase. Nucleic Acids Research 40: D706–D714.
59. KoflerR, SchlöttererC (2012) Gowinda: unbiased analysis of gene set enrichment for genome-wide association studies. Bioinformatics 28: 2084–2085.
60. FederAF, PetrovDA, BerglandAO (2012) LDx: Estimation of linkage disequilibrium from high-throughput pooled resequencing data. PLoS ONE 7: e48588 doi:10.1371/journal.pone.0048588.t002
61. Fiston-LavierA-S, SinghND, LipatovM, PetrovDA (2010) Drosophila melanogaster recombination rate calculator. Gene 463: 18–20.
62. Hedrick PW (2009) Genetics of populations. Pp.491. Jones & Bartlett Learning Press.
63. Kalinowski (2005) Do polymorphic loci require large sample sizes to estimate genetic distances? Heredity 94: 33–36.
64. JoeH (2006) Generating Random Correlation Matrices Based on Partial Correlations. Journal of Multivariate Analysis 97: 2177–2189.
65. Christiansen FB (2000) Population genetics of multiple loci. Pp. 32–40. Wiley series in mathematical and computational biology. John Wiley & Sons Inc. Hoboken, NJ.
Štítky
Genetika Reprodukčná medicínaČlánok vyšiel v časopise
PLOS Genetics
2014 Číslo 8
- Je „freeze-all“ pro všechny? Odborníci na fertilitu diskutovali na virtuálním summitu
- Gynekologové a odborníci na reprodukční medicínu se sejdou na prvním virtuálním summitu
Najčítanejšie v tomto čísle
- Meta-Analysis of Genome-Wide Association Studies in African Americans Provides Insights into the Genetic Architecture of Type 2 Diabetes
- KDM6 Demethylase Independent Loss of Histone H3 Lysine 27 Trimethylation during Early Embryonic Development
- The RNA Helicases AtMTR4 and HEN2 Target Specific Subsets of Nuclear Transcripts for Degradation by the Nuclear Exosome in
- EF-P Dependent Pauses Integrate Proximal and Distal Signals during Translation